Introduction
As noted by Malhotra (2000), despite the lack of ‘‘commonly agreed upon definition of knowledge management’’ organisations both in the public and private sectors are getting increasingly interested in the topic. This paper reviews a sample of definitions of Knowledge Management (KM) that have been proposed by various scholars who have conducted research on this emerging subject. To do this, various definitions of ‘knowledge’, especially in relation to ‘information’ and ‘data’ will be discussed to enable a holistic view of the concept of KM. At the end of this paper, an attempt will be made to describe knowledge management by combining elements of the various definitions that will be discussed.
Knowledge, Information and Data.
The distinction between Knowledge, information and data has always been a subject of much interest in the academia due to the lines that exist between the definitions of each word, especially between knowledge and information. Chaba (2008), suggests a distinction by giving an example where ‘£150’ is just data, ‘£150 pounds on gas bill’ is information, and ‘If I have £150, I have got enough money to pay my Gas Bill’ is knowledge. This example seems to suggest that ‘information’ is ‘data’ with some meaning, and knowledge is a combination of some information and a context. Implicit in this example is the ‘knowledge’ that having £150 pounds means that the gas bill can be paid, that is having £150 and knowing that this amount can pay the gas bill.
The distinction provided by the example given above is similar to that given by Kock Jr et al (1996), where its was suggested that although Knowledge, Information and Data are not synonymous, they are interrelated and have no useful existence without each other. To compare the three words within an organisational context, Kock Jr et al (1996) gave an example of a situation where a division manager interprets data (e.g. Productivity figures) in a context (e.g. in a meeting with a manager in one of the division’s plant), as information (e.g. the productivity figures are low). This information is then combined with knowledge (e.g. if we have a new lathe in operation the production will go up), within a domain (e.g. the plant’s assembly line), to produce effective action (e.g. introduce a new lathe into the plant’s assembly line). Another useful distinction made by Kock Jr et al (1997) is that while ‘information’ is descriptive – relating to the past and the present - , ‘knowledge’ is mostly predictive as it provides the basis for predicting the future, with a degree of certainty, using information about the past and the present.
According to Stenmark (2002), Figure 1 below is commonly used to describe the relationship between these terms but the arguments are flawed as they are based on wrong assumptions that the relationship between data, information and knowledge is linear, asymmetric (data is transformed into information, and information is transformed into knowledge), and that knowledge is superior to information and information is superior to data.
Fig 1 Data, Information and Knowledge
Source: Stenmark (2002)
Toumi 1999 (cited in Stenmark 2002) , challenged this view by suggesting that ‘knowledge’ embedded in the human mind can be instantiated to form explicit ‘information’ which can subsequently be coded into pure data which has the highest value from an IS/IT perspective; and since computers can only effectively process data, it should be on top of the value hierarchy.
Stenmark (2002) however argues that both arguments presented above are erroneous and that data, information and knowledge are interwoven in more complex ways because they influence each other and the value of any of them is dependent on the purpose for which it is being used. ‘Data’ and ‘information’ requires ‘knowledge’ to be interpretable and likewise, new ‘knowledge’ is constructed using existing ‘data’ and ‘information’. In the words of Stenmark (2002), ‘‘since a piece of text itself is not sufficient to exhaustively describe the knowledge to which it refers, the reader's tacit knowledge must be compatible with that of the writer in order to interpret and fully comprehend the implications of the information. Hence, what one conceives as information another sees as data’’
In essence the relationship between ‘data’, ‘information’ and ‘knowledge’ is very complicated and to understand any one of them, there must be a context and a purpose. What is regarded as mere ‘data’ may be seen as ‘information’ by another person or even by the same person in a different context or situation. From the arguments put forward so far in this paper, ‘knowledge’ can thus be likened to an understanding or cognition of a specific domain area or subject exhibited by an individual as a result of his experience or just gut feeling; can either be expressed in form of information or data or which the individual may not be able to make explicit; and which can enable effective action. Having put ‘knowledge’ in a proper perspective in relation to ‘data’ and ‘information’, this paper progresses with an analysis of various definitions of ‘Knowledge’ and Knowledge Management (KM).
Knowledge Management (KM) Definitions
Beijerse (2000), defined knowledge as follows;
‘‘knowledge is seen here as information; the capability to interpret data and information through a process of giving meaning to these data and information; and an attitude aimed at wanting to do so’’.
This definition is based on other definitions where knowledge is seen as ‘something more than information’. As suggested by Beijerse (1999), a popular distinction - first made by Michael Polanyi – is that made between tacit and explicit knowledge. Polanyi (1966), cited in Beijerse (1999), stated that because people acquire knowledge by active (re)creation and organisation of their own experience, tacit or personal knowledge is crucial to human cognition and knowledge that can be expressed in words and numbers is just a tip of the iceberg. Implicit in this definition is the distinction between tacit and explicit knowledge which complement and influence each other in the creative actions of people.
KM, Beijerse (1999) also came up with a derived definition of managements as follows;
‘‘Management is the strategy-driven motivation and facilitation of people, aimed at reaching the organizational goals’’
This definition according to Beijerse (1999) is predicated on the central elements of management including the formulation of a strategy, ensuring that the strategy is realized, organization as a tool fulfilling the first two elements and the people who manage and are managed within the organisation.
Looking at the two definitions above, Beijerse (1999) suggests that KM is more specific than management in that while management is about motivating and stimulating people to achieve specified goals, KM focuses more on one aspect of people – their knowledge.
Different authors have defined KM in different ways. Some focus on reference to KM as the process of managing organisations’ intangible assets. An example of these is Sveiby (cited by Beijerse, 1999), who defined KM as ‘‘the art of creating value from an organization's intangible assets’’. Den Hertog and Huizinga, (1997), as cited by Beijerse (1999), placed emphasis on the choice organisations make regarding their core competencies referred to as ‘knowledge ambition’ and thus defined KM as ‘‘using instruments to realize the knowledge ambition’’. Beijerse (1999) also cited the work of Mathieu Weggeman (1997) who focused on the ‘knowledge value chain involving four successive constituent processes. These are first, determination of the strategic need for knowledge; second, determination of the knowledge gap which is quantitative and qualitative difference between needed and available knowledge in the organisation; third, narrowing of the knowledge gap by developing knowledge, buying knowledge, improving on existing knowledge, or getting rid of outdated or irrelevant knowledge; and fourth, dissemination and application of available knowledge to serve the interest of customers and other stakeholders. Weggeman (1997), (according to Beijerse, 1999) pays less attention to information technology and more attention to the strategic, personal, organisational and cultural aspects of KM and thus defined KM as
‘‘arranging and managing the operational processes in the knowledge value chain in such a way that realizing the collective ambition, the targets and the strategy of the organization is being promoted’’.
Beijerse (1997) put more emphasis on the importance of tacit knowledge, seeing this as added value, and having the earlier definition of ‘knowledge’ and ‘management’ in mind, defined KM as
‘‘achieving organizational goals through the strategy-driven motivation and facilitation of (knowledge-) workers to develop, enhance and use their capability to interpret data and information (by using available sources of information, experience, skills, culture, character, personality, feelings, etc.) through a process of giving meaning to these data and information.’’
In explaining this definition of KM, Beijerse (1999) put the giving of meaning to data and information to create knowledge as the core of the KM process. Beijerse (1999) also agreed with Nonaka and Takeuchi (1995), and Van der Spek and Spijkervet (1996) that knowledge is a vibrant human process in which truth is created; and with the implied conclusion that there is no such thing as one truth or one possibility of knowing the truth. According to Beijerse (1999), knowledge should be judged on its true merit and what is knowledge for one organisation could be worthless data for another.
Beijerse (2000) goes ahead to identify nine possible knowledge streams in KM which essential for business leaders to think about in using knowledge to achieve organisational goals. These are determination of necessary knowledge, determination of available knowledge, determination of knowledge gap, knowledge development, knowledge acquisition, knowledge lock (changing developed or purchased knowledge into a systematic or structural form made available to everyone), knowledge sharing, knowledge utilization and knowledge evaluation. As noted by Beijerse (2000) the output from knowledge evaluation process forms an input into the process of identifying knowledge gap, thus the whole KM process becomes cyclical.
Another major view of what KM should be was presented by Malhotra (2000), who presented a new perspective on how organisations should view knowledge management. This is necessitated by transition in the last quarter of the twentieth century from Information technology as a lever of competitive advantage to information being viewed as a ‘utility’ and more recently the ‘e-everything phenomena’ where the Internet and electronic commerce have become key factors in business and IT strategy. According to Malhotra (2000), there has been a paradigm shift from Total Quality Management (TQM) – focusing on continuous improvement in existing business processes – to Business Process Reengineering (BPR) which emphasized IT intensive radical redesign of business process. However, according to Malhotra (2000), BPR could not measure up to the Networked paradigm enabled by the Internet and WWW as its focus was on co-ordination of companies internal function and it scope could not cover information flows with an organisation’s customers and supplier whose roles were becoming increasingly more important.
Malhotra (2000) further stated that given the unpredictable nature of the new era there was need for new paradigm shift from transformation at the level of business processes to a radical rethinking of the overall business model as well as information flows between organisations and industries. Organisations’ survival will depend on their ability to continuously adapt the programmed logic supporting their business models and business process to the continual dynamic and radical changes in the business environment. Bearing this argument in mind Malhotra (2000) came up with the following definition of KM,
‘‘Knowledge management caters to the critical issues of organizational adaptation, survival, and competence in face of increasingly discontinuous environmental change. Essentially, it embodies organizational processes that seek synergistic combination of data and information-processing capacity of information technologies, and the creative and innovative capacity of human beings.’’
According to Malhotra (2000), this definition is predicated on the need to have a synergy between the capabilities of advanced technologies and human creativity and innovation to realize the agility demanded by emerging business environment.
Conclusions
From the definitions given above KM can be described as a process by which organizations strategize to achieve their goals through the creation, dissemination and application of the relevant knowledge. This is achieved by motivating and inspiring the members of the organization to continue to exploit their human innovative capabilities to develop new ways of achieving the goals of the organization or develop new goals (business model rethinking) for the organization given the constant and dynamic changes in the operating environment. The process of knowledge creation, dissemination and application is actively supported by the use of emerging advanced information systems to enhance knowledge sharing within the organization and with other external stakeholders.
The concepts described in this article can be - and are being applied (albeit unconsciously) - to influence management practice relating to the management of knowledge within organizations. The paper by Malhotra (2000) offers a fundamentally new perspective to KM by suggesting that organizations must be prepared to continuously rethink their business models (not just business processes) to survive in today’s Internet age given the increased awareness by all stakeholders, especially customers and suppliers and the unpredictable changes occurring in the business environment. To do this, Knowledge workers must be encouraged and motivated to innovate and come up with new products and services to meet the ever changing and increasing demands of customers.
Organizations need to create a learning environment to ensure that its members are well informed, understand, and share the need for a radical rethinking of the business models. This argument was supported by real –life organizations like Royal Dutch Shell where the strategy session focused on differences in the perception of different managers to enable an understanding of the multiple world views of the future. Another example is dynamic pricing models and comparison-shopping agents such as mySimon who take dynamically changing market prices into consideration.
References
Beijerse, R.P (1999), ‘‘Questions in knowledge management: defining and conceptualizing a phenomenon.’’. Journal of Knowledge Management. Vol. 3 No. 2 pp 94 – 109
Beijerse, R.P (2000), ‘‘Knowledge management in small and medium sized companies: knowledge management for entrepreneurs.’’ Journal of knowledge management, Vol. 4 No. 2 pp 162 – 179.
Chaba, A, 2008, ‘‘data, information and knowledge’’, Knowledge Management Blog available at http://abouyounesmdx.blogspot.com accessed on March 3, 2008.
Kock, N.F, McQueen, R.J. and Corner, J.L. (1997), ‘‘the nature of data, information and knowledge exchanges in business processes: implications for process improvement and organizational learning’’, The Learning organization, Vol. 3, No. 2, pp.70-80
Kock, N.F, McQueen, R.J. and Baker, M. (1996), ‘‘Learning and Process improvement in knowledge organisations: a critical analysis of four contemporary myths’’. The learning organisation, Vol.3, No.1, pp.31-41.
Malhotra, Y (2000), ‘‘Knowledge management for E-business performance: advancing information strategy to internet time’’ Information Strategy, the executives journal, Vol 16, No 4 pp 5 – 16 available at http://www.brint.com/members/online/200503/kmebiz.pdf
Stenmark, D. (2002), ‘‘Information Vs Knowledge: The role of Intranets in Knowledge Management’’. In proceedings of HICSS 35, IEEE Press, Hawai, January 7-10, 2002.
As noted by Malhotra (2000), despite the lack of ‘‘commonly agreed upon definition of knowledge management’’ organisations both in the public and private sectors are getting increasingly interested in the topic. This paper reviews a sample of definitions of Knowledge Management (KM) that have been proposed by various scholars who have conducted research on this emerging subject. To do this, various definitions of ‘knowledge’, especially in relation to ‘information’ and ‘data’ will be discussed to enable a holistic view of the concept of KM. At the end of this paper, an attempt will be made to describe knowledge management by combining elements of the various definitions that will be discussed.
Knowledge, Information and Data.
The distinction between Knowledge, information and data has always been a subject of much interest in the academia due to the lines that exist between the definitions of each word, especially between knowledge and information. Chaba (2008), suggests a distinction by giving an example where ‘£150’ is just data, ‘£150 pounds on gas bill’ is information, and ‘If I have £150, I have got enough money to pay my Gas Bill’ is knowledge. This example seems to suggest that ‘information’ is ‘data’ with some meaning, and knowledge is a combination of some information and a context. Implicit in this example is the ‘knowledge’ that having £150 pounds means that the gas bill can be paid, that is having £150 and knowing that this amount can pay the gas bill.
The distinction provided by the example given above is similar to that given by Kock Jr et al (1996), where its was suggested that although Knowledge, Information and Data are not synonymous, they are interrelated and have no useful existence without each other. To compare the three words within an organisational context, Kock Jr et al (1996) gave an example of a situation where a division manager interprets data (e.g. Productivity figures) in a context (e.g. in a meeting with a manager in one of the division’s plant), as information (e.g. the productivity figures are low). This information is then combined with knowledge (e.g. if we have a new lathe in operation the production will go up), within a domain (e.g. the plant’s assembly line), to produce effective action (e.g. introduce a new lathe into the plant’s assembly line). Another useful distinction made by Kock Jr et al (1997) is that while ‘information’ is descriptive – relating to the past and the present - , ‘knowledge’ is mostly predictive as it provides the basis for predicting the future, with a degree of certainty, using information about the past and the present.
According to Stenmark (2002), Figure 1 below is commonly used to describe the relationship between these terms but the arguments are flawed as they are based on wrong assumptions that the relationship between data, information and knowledge is linear, asymmetric (data is transformed into information, and information is transformed into knowledge), and that knowledge is superior to information and information is superior to data.
Fig 1 Data, Information and Knowledge
Source: Stenmark (2002)
Toumi 1999 (cited in Stenmark 2002) , challenged this view by suggesting that ‘knowledge’ embedded in the human mind can be instantiated to form explicit ‘information’ which can subsequently be coded into pure data which has the highest value from an IS/IT perspective; and since computers can only effectively process data, it should be on top of the value hierarchy.
Stenmark (2002) however argues that both arguments presented above are erroneous and that data, information and knowledge are interwoven in more complex ways because they influence each other and the value of any of them is dependent on the purpose for which it is being used. ‘Data’ and ‘information’ requires ‘knowledge’ to be interpretable and likewise, new ‘knowledge’ is constructed using existing ‘data’ and ‘information’. In the words of Stenmark (2002), ‘‘since a piece of text itself is not sufficient to exhaustively describe the knowledge to which it refers, the reader's tacit knowledge must be compatible with that of the writer in order to interpret and fully comprehend the implications of the information. Hence, what one conceives as information another sees as data’’
In essence the relationship between ‘data’, ‘information’ and ‘knowledge’ is very complicated and to understand any one of them, there must be a context and a purpose. What is regarded as mere ‘data’ may be seen as ‘information’ by another person or even by the same person in a different context or situation. From the arguments put forward so far in this paper, ‘knowledge’ can thus be likened to an understanding or cognition of a specific domain area or subject exhibited by an individual as a result of his experience or just gut feeling; can either be expressed in form of information or data or which the individual may not be able to make explicit; and which can enable effective action. Having put ‘knowledge’ in a proper perspective in relation to ‘data’ and ‘information’, this paper progresses with an analysis of various definitions of ‘Knowledge’ and Knowledge Management (KM).
Knowledge Management (KM) Definitions
Beijerse (2000), defined knowledge as follows;
‘‘knowledge is seen here as information; the capability to interpret data and information through a process of giving meaning to these data and information; and an attitude aimed at wanting to do so’’.
This definition is based on other definitions where knowledge is seen as ‘something more than information’. As suggested by Beijerse (1999), a popular distinction - first made by Michael Polanyi – is that made between tacit and explicit knowledge. Polanyi (1966), cited in Beijerse (1999), stated that because people acquire knowledge by active (re)creation and organisation of their own experience, tacit or personal knowledge is crucial to human cognition and knowledge that can be expressed in words and numbers is just a tip of the iceberg. Implicit in this definition is the distinction between tacit and explicit knowledge which complement and influence each other in the creative actions of people.
KM, Beijerse (1999) also came up with a derived definition of managements as follows;
‘‘Management is the strategy-driven motivation and facilitation of people, aimed at reaching the organizational goals’’
This definition according to Beijerse (1999) is predicated on the central elements of management including the formulation of a strategy, ensuring that the strategy is realized, organization as a tool fulfilling the first two elements and the people who manage and are managed within the organisation.
Looking at the two definitions above, Beijerse (1999) suggests that KM is more specific than management in that while management is about motivating and stimulating people to achieve specified goals, KM focuses more on one aspect of people – their knowledge.
Different authors have defined KM in different ways. Some focus on reference to KM as the process of managing organisations’ intangible assets. An example of these is Sveiby (cited by Beijerse, 1999), who defined KM as ‘‘the art of creating value from an organization's intangible assets’’. Den Hertog and Huizinga, (1997), as cited by Beijerse (1999), placed emphasis on the choice organisations make regarding their core competencies referred to as ‘knowledge ambition’ and thus defined KM as ‘‘using instruments to realize the knowledge ambition’’. Beijerse (1999) also cited the work of Mathieu Weggeman (1997) who focused on the ‘knowledge value chain involving four successive constituent processes. These are first, determination of the strategic need for knowledge; second, determination of the knowledge gap which is quantitative and qualitative difference between needed and available knowledge in the organisation; third, narrowing of the knowledge gap by developing knowledge, buying knowledge, improving on existing knowledge, or getting rid of outdated or irrelevant knowledge; and fourth, dissemination and application of available knowledge to serve the interest of customers and other stakeholders. Weggeman (1997), (according to Beijerse, 1999) pays less attention to information technology and more attention to the strategic, personal, organisational and cultural aspects of KM and thus defined KM as
‘‘arranging and managing the operational processes in the knowledge value chain in such a way that realizing the collective ambition, the targets and the strategy of the organization is being promoted’’.
Beijerse (1997) put more emphasis on the importance of tacit knowledge, seeing this as added value, and having the earlier definition of ‘knowledge’ and ‘management’ in mind, defined KM as
‘‘achieving organizational goals through the strategy-driven motivation and facilitation of (knowledge-) workers to develop, enhance and use their capability to interpret data and information (by using available sources of information, experience, skills, culture, character, personality, feelings, etc.) through a process of giving meaning to these data and information.’’
In explaining this definition of KM, Beijerse (1999) put the giving of meaning to data and information to create knowledge as the core of the KM process. Beijerse (1999) also agreed with Nonaka and Takeuchi (1995), and Van der Spek and Spijkervet (1996) that knowledge is a vibrant human process in which truth is created; and with the implied conclusion that there is no such thing as one truth or one possibility of knowing the truth. According to Beijerse (1999), knowledge should be judged on its true merit and what is knowledge for one organisation could be worthless data for another.
Beijerse (2000) goes ahead to identify nine possible knowledge streams in KM which essential for business leaders to think about in using knowledge to achieve organisational goals. These are determination of necessary knowledge, determination of available knowledge, determination of knowledge gap, knowledge development, knowledge acquisition, knowledge lock (changing developed or purchased knowledge into a systematic or structural form made available to everyone), knowledge sharing, knowledge utilization and knowledge evaluation. As noted by Beijerse (2000) the output from knowledge evaluation process forms an input into the process of identifying knowledge gap, thus the whole KM process becomes cyclical.
Another major view of what KM should be was presented by Malhotra (2000), who presented a new perspective on how organisations should view knowledge management. This is necessitated by transition in the last quarter of the twentieth century from Information technology as a lever of competitive advantage to information being viewed as a ‘utility’ and more recently the ‘e-everything phenomena’ where the Internet and electronic commerce have become key factors in business and IT strategy. According to Malhotra (2000), there has been a paradigm shift from Total Quality Management (TQM) – focusing on continuous improvement in existing business processes – to Business Process Reengineering (BPR) which emphasized IT intensive radical redesign of business process. However, according to Malhotra (2000), BPR could not measure up to the Networked paradigm enabled by the Internet and WWW as its focus was on co-ordination of companies internal function and it scope could not cover information flows with an organisation’s customers and supplier whose roles were becoming increasingly more important.
Malhotra (2000) further stated that given the unpredictable nature of the new era there was need for new paradigm shift from transformation at the level of business processes to a radical rethinking of the overall business model as well as information flows between organisations and industries. Organisations’ survival will depend on their ability to continuously adapt the programmed logic supporting their business models and business process to the continual dynamic and radical changes in the business environment. Bearing this argument in mind Malhotra (2000) came up with the following definition of KM,
‘‘Knowledge management caters to the critical issues of organizational adaptation, survival, and competence in face of increasingly discontinuous environmental change. Essentially, it embodies organizational processes that seek synergistic combination of data and information-processing capacity of information technologies, and the creative and innovative capacity of human beings.’’
According to Malhotra (2000), this definition is predicated on the need to have a synergy between the capabilities of advanced technologies and human creativity and innovation to realize the agility demanded by emerging business environment.
Conclusions
From the definitions given above KM can be described as a process by which organizations strategize to achieve their goals through the creation, dissemination and application of the relevant knowledge. This is achieved by motivating and inspiring the members of the organization to continue to exploit their human innovative capabilities to develop new ways of achieving the goals of the organization or develop new goals (business model rethinking) for the organization given the constant and dynamic changes in the operating environment. The process of knowledge creation, dissemination and application is actively supported by the use of emerging advanced information systems to enhance knowledge sharing within the organization and with other external stakeholders.
The concepts described in this article can be - and are being applied (albeit unconsciously) - to influence management practice relating to the management of knowledge within organizations. The paper by Malhotra (2000) offers a fundamentally new perspective to KM by suggesting that organizations must be prepared to continuously rethink their business models (not just business processes) to survive in today’s Internet age given the increased awareness by all stakeholders, especially customers and suppliers and the unpredictable changes occurring in the business environment. To do this, Knowledge workers must be encouraged and motivated to innovate and come up with new products and services to meet the ever changing and increasing demands of customers.
Organizations need to create a learning environment to ensure that its members are well informed, understand, and share the need for a radical rethinking of the business models. This argument was supported by real –life organizations like Royal Dutch Shell where the strategy session focused on differences in the perception of different managers to enable an understanding of the multiple world views of the future. Another example is dynamic pricing models and comparison-shopping agents such as mySimon who take dynamically changing market prices into consideration.
References
Beijerse, R.P (1999), ‘‘Questions in knowledge management: defining and conceptualizing a phenomenon.’’. Journal of Knowledge Management. Vol. 3 No. 2 pp 94 – 109
Beijerse, R.P (2000), ‘‘Knowledge management in small and medium sized companies: knowledge management for entrepreneurs.’’ Journal of knowledge management, Vol. 4 No. 2 pp 162 – 179.
Chaba, A, 2008, ‘‘data, information and knowledge’’, Knowledge Management Blog available at http://abouyounesmdx.blogspot.com accessed on March 3, 2008.
Kock, N.F, McQueen, R.J. and Corner, J.L. (1997), ‘‘the nature of data, information and knowledge exchanges in business processes: implications for process improvement and organizational learning’’, The Learning organization, Vol. 3, No. 2, pp.70-80
Kock, N.F, McQueen, R.J. and Baker, M. (1996), ‘‘Learning and Process improvement in knowledge organisations: a critical analysis of four contemporary myths’’. The learning organisation, Vol.3, No.1, pp.31-41.
Malhotra, Y (2000), ‘‘Knowledge management for E-business performance: advancing information strategy to internet time’’ Information Strategy, the executives journal, Vol 16, No 4 pp 5 – 16 available at http://www.brint.com/members/online/200503/kmebiz.pdf
Stenmark, D. (2002), ‘‘Information Vs Knowledge: The role of Intranets in Knowledge Management’’. In proceedings of HICSS 35, IEEE Press, Hawai, January 7-10, 2002.
